The prior art reflects the continuing challenge to obtain accurate and precise measurements under conditions wherein many experimental conditions are difficult to control. Significantly, the prior art is silent with regard to the influence of random factors on accuracy, statistical significance, and the information content of data used to derive relevant mathematical curves. The methods and protocols presented in relevant technical papers describe a typical embodiment of quantitative analysis that displays wide data dispersion and low correlation coefficients. The prior art highlights an important point that the obstacles to precision become more difficult for test measurements of biological, chemical, and physical functions at the molecular level.
Prior art is described herein on pages 1-3. The failures of prior art are demonstrated by the omissions in prior patents. Prior art recognizes the necessity of constructing a calibration curve (Akhavan-Tafti, 2007, paragraph 0044) and the importance of an internal standard (Chandler 2001, paragraph 0025). However, the methods described result in only an external standard (Chandler 2001, claims 1 through 15) such as daily calibration for machine-to-machine differences (Chandler 2005, paragraph 0129).
The use of even basic internal positive and negative standards for calibration has been shown to improve assay reliability in biochemical experiments using bead arrays (Martins 2002; Hanley 2007). Internal standards are widely used in analytical chromatography, Western blots, quantitative PCR, and quantitative mass spectrometry. The reason for the inclusion of internal standards is that they are especially useful for analyses in which the quantity and quality of sample varies from run to run for reasons that are difficult to control.
For example, gas chromatography/mass spectrometry-based analysis of pollutants demonstrated that internal standards corrected for systematic errors while external standards introduced bias that affected measurement accuracy (Kirchmer 1983). In quantitative polymerase chain reactions (PCR) an internal control gene is used to normalize samples for measures of relative abundance (Livak 2001). Similarly, in quantitative mass spectrometry a control sample is detected simultaneously to provide relative abundance by direct comparison to experimental samples (Ong 2002).
In these cases, quantitation is carried out by a direct ratio of signal obtained from the experimental sample divided by the signal obtained from the control sample. These methods do not define mathematical functions established by statistical parameters that correct for detector response and uncontrollable variables in handling that influence abundance between samples. Indeed, in the case of quantitative mass spectrometry, control samples are prepared and handled independently from experimental samples, thereby excluding them from providing accurate quantitation beyond instrument calibration.
Although internal positive and negative controls have been included in prior art, internal standard curves that define a mathematical function through more than one calibrated internal standard have not been implemented to quantitatively analyze experimental samples. Although prior art includes internal standards, the standards are used to compare detector performance between samples. Thus, the internal standards serve as basic comparisons for detector calibration instead of quantitative tools for sample-specific analyses.
For assays that measure multiple components of a reaction, an additional concern is the normalization of output contributions by various components of the reaction. For example, while multiple peptides are readily detected in a single scan by mass spectrometry, the difference in ionization potential between phosphorylated and un-phosphorylated forms results in over-representation of one species over another (Busman, Schey et al. 1996). Internal standards for one molecular species are irrelevant for the other. Accordingly, only ratios of observed abundance can be obtained to describe relative relationships without statistical significance.
Generally, the prior art does not provide a sufficient number of internal controls to ensure accurate measurements of test samples against calibrated standards. The prior art does not use internal standards to quantitatively analyze the test samples with statistical confidence. Although the prior art uses calibrated standards, the prior art does not force all measurement conditions for the standard and the test sample to be identical. Technical challenges associated with standardizing reagents used in chemical and biological methods result in inherent problems caused by uncontrolled variations between samples and between laboratories.
For example, high-throughput biological screens often note that the organization of samples within plates and small variations between plates can lead to strong sample bias (Koren, Tirosh et al. 2007). Significantly, even basic quantitation in chemistry and biology requires external standards for a combination of background subtraction resulting from non-specific interactions in complex samples, and the calculation of proportions relative to a baseline (Xiaoming, Syuhei et al. 2008).
Although several papers discuss internal controls, the typical methods described depend on external standards and are therefore not sensitive to uncontrollable variables between samples. For many circumstances, an internal standard is only used as a baseline to provide a proportionate measure of abundance for experimental samples. In most cases, the prior art fails to provide a method to simultaneously measure both the control standard and the test sample simultaneously, as an integral part of the test sample. Because prior art necessarily includes inherent variation in test conditions that involve separate controls and test samples, the result is significant inaccuracy. The prior art that does include internal controls uses them as calibration standards and does not allow multiple internal control standards to provide multiple simultaneous comparisons of the test sample versus the control standards or the control standards versus each other.
The following is a discussion of problems with the prior art and the resulting need for a new method that would resolve the important measurement issues.
The Requirement for Accuracy
Extreme precision is required for measurements at the molecular level, such as enzyme activity. Despite the importance of accuracy, the prior art shown in current publications result in high data dispersion and resulting low confidence in the test results, even after selective exclusion of outlier data. As a result, many authors fail to show the statistical confidence intervals in data plots, and typical functional curves exhibit low correlation coefficients. Typically, the resulting wide data dispersion supports only a rough figure of merit (Zhang, Chung et al. 1999).
This invention solves these difficulties by establishment of a standard curve based on multiple data points for each reaction to provide a calibrated standard response. In this way, the previously uncontrolled factors are measured with precision, so that sample data from each test can be meaningfully compared. This novel method uses an internal standard curve for each test and for triplicate groups of tests during an experiment. This method provides a standard for comparison that prevents imprecision caused by subtle but uncontrolled variables. Therefore, this method provides a practical solution to the critical need for high accuracy measurements under conditions wherein many variables are extremely difficult to control.
This novel method is applicable to the broad range of test equipment and procedures for precision measurements required for biochemistry, biophysics, and chemical engineering. By contrast with prior art, the specific protocol described by this novel method results in minimal data dispersion, calculation of the mean with narrow confidence intervals at the 0.01 level, and derivation of precise mathematical curves based on the least-squares fit with an unusually high correlation coefficient. This method establishes calibrated internal controls with statistical relevance to avoid the data dispersion caused by uncontrollable variables.
The Requirement for Comprehensive Internal Standards
By definition, an internal standard must be based on each sample, and measure all relevant variables that could reasonably affect the test. To qualify as an internal standard, the calibrated standard control must be established for the test conditions for each sample. For the accuracy required for measurements at the molecular level, an internal calibration standard must be based on samples drawn from a specific segregated population under controlled test conditions. As a critical flaw, the calibration to external standards has the inherent risk of changed test circumstances due to variables that are very difficult to control.
The methods to develop and apply an internal standard curve for quantitation of individual sample data are unique. Significantly, the internal standard curves provide the basis for reaction-specific quantitation while controlling for detector bias and uncontrollable factors from handling procedures such as non-homogenous distributions. Internal standards are a practical requirement for analyses that exhibit differences in sample quantity and quality for different runs. For many types of test equipment, internal standard curves are rare because most platforms do not allow for the analytical separation of multiple components in the same test sample.
For many types of laboratory tests, internal controls are a practical necessity due to many variables that are difficult to control, such as sample variables or test conditions. For example, establishment of internal standards is necessary for quantitative chromatography and mass spectrometry, where injector and detector performance with small volumes is not reproducible. By contrast, establishment of internal standards remains a challenge for many test methods, such as Western blot, antibody-based studies, and array equipment.
Statistical inference is a practical necessity to establish the accuracy of the test measurements. Definitions of terms used for statistical inference depend on the circumstances under which tests are performed. As applied to array equipment, the population is defined as each reaction in the array from which samples are drawn. The score is the measured signal intensity. The sample is the number of individual units analyzed by a single use of the sampling device. The stratified sample is defined as the combined results of triplicate tests designed to exhibit identical conditions that can be controlled.